The present invention relates to memristive devices. More specifically, the present invention relates to a memristive device based on alkali-doping of transitional metal oxides.
“Machine learning” is used to broadly describe a primary function of electronic systems that learn from data. In accelerated machine learning and cognitive science, artificial neural networks (ANNs) are a family of statistical learning models inspired by the biological neural networks of animals, and in particular the brain. ANNs can be used to estimate or approximate systems and functions that depend on a large number of inputs and are generally unknown. ANN architectures, neuromorphic microchips and ultra-high density nonvolatile memory can be formed from high density, low cost circuit architectures known as cross-bar arrays. A basic crossbar array configuration includes a set of conductive row wires and a set of conductive column wires formed to intersect the set of conductive row wires. The intersections between the two sets of wires are separated by so-called crosspoint devices, which can be formed from thin film material. Cross-point devices can be implemented as so-called memristive devices. Characteristics of a memristive device include non-volatility, the ability to store a variable resistance value, and the ability to tune up or tune down a resistance using current or voltage pulses.